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SIAS Bio-IT Conference_FINAL

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SIAS Bio-IT Conference_FINAL

  1. 1. Scientific Information as a Business Asset Driving Productivity at Merck Research Labs Through Novel Approaches to Scientific Information Management Speaker: John Koch Merck & Co.
  2. 2. 2 Overview • Information Management Challenges Currently Facing R&D Organizations • The Value of Better Information Management • Merck’s Scientific Information Architecture and Search (SIAS) Group • Approaches for Improving Information Management
  3. 3. 3 R&D decisions rely on high quality information to steer programs and the pipeline 145 Knowledge Assets “Target validation plan” 250 Business Groups “Early Development team” 1849 People “John Smith” 1144 Information Types “Clinical Trial Name” 110 Organization Units “Analytical Chemistry” 492 Sources “Electronic Lab Notebook” 66 Business Processes “Integrative assessment of liver toxicity” 86 Decisions/ Gateways “Determine Patient Stratification Biomarkers” 472 Activities “Refine model” 125 Roles “Statistician” R&D Information LandscapeR&D decisions rely on high quality information to steer programs and the pipeline Over time BioPharma has created and stored tremendous amounts of data, information and knowledge; there are 100,000’s of information elements Companies must make effective, efficient use of the vast quantity of information it houses, creates, and has access to externally to make sound decisions The volume and sophistication of internal information and that available through external sources continues to grow at a rapid and accelerating rate Therefore, the ability to readily find, access, and use information is absolutely critical
  4. 4. 4 The Problem 1000’s people 100’s information types 1000’s repositories 100’s decisions 100,000’s knowledge assets Scannell et al. 2012 Nature Rev. Drug Disc. 11, 191 100’s teams $ Information Complexity
  5. 5. 5 KnowledgeInformationData Combine internal and external data Integrate & Analyze Present Decide Culture of Single Use
  6. 6. 6 5 Today Next 2-3 Years Beyond Culture of Single Use “Find & Access” DecisionMaking Quality Vocabulary Management Embedded Stewardship Information Flows Modeled Effective Search Integrated Information Architecture IM Challenges Characterized Fragmented tools, processes Systematic categorization of data Information ManagementMaturity As knowledge workers understand and embrace improved information management practices, better decision making can be enabled by better access to information Organization-Wide Information Re-Use ? Better Information Management  Better Decision Making: Better analysis, more transparency and collaboration, better workflow management, faster decisions DecisionQualityAdoption,Maturity Improving R&D Decision Making
  7. 7. 7 5 Engaging the business: Focus Area Key Questions User Interface Engine Content Creators Creators ContentEngineQuery Results Interface What information is required to make those decisions? Who needs that information? How do they use that information used to make those decisions?2 What are the critical business processes? What major decisions are associated with those processes?1 How is that information created? Who creates it? Where is that information stored?3 How is that information accessed (searched for, found, displayed)?4 What challenges are associated with accessing and using that information?5 How can access to and use of that information be improved? What value will those improvements deliver to the business? 6 Users Morville & Callendar. 2010 Search Patterns
  8. 8. 8 Information Management CapabilitiesArchitectureSearchAccess IM Capabilities Description Search tools that enable users to locate scientific information across various sources, both structured and unstructured, in various formats and across functional groups Capability for users to identify colleagues with specific skills, expertise, or tacit knowledge through a search tool and / or standardized profiles or tagging System of access policies that prudently permits access to information and has clear procedures for granting or restricting access Shared practices for creating, storing, sharing, and maintaining explicit and tacit information Organization of critical data sources to make them more conducive to search, retrieval, analysis and re-use through techniques including tagging and indexing Well-maintained record of critical information and data sources across the organization, including how the information is used or linked to other sources Improving Information Management requires specific capabilities to enhance information search, access, and architecture 1 2 3 4 5 6 Expertise Location Access Data Stewardship Data Structuring Key Data Assets Scientific Search
  9. 9. 9 ILLUSTRATIVE Leaders in Search & Information Management:  Indexing of complex hierarchical relationships from relational database tables  Multi-faceted, interactive filtering of search results based on document metadata  Implementing solutions for searching non-text information (e.g., enterprise video search)  Advanced search analytics  Integration with social media  Highly scalable / extensible Service-Oriented Architecture  Seamless information flow between departments / sites  Includes a data services and exchange layer  Reusable and configurable code modules  Closed-loop data flow via integrated data sources across the product life cycle  Consistent, personalized, real-time access for internal and external users  Enterprise-wide technology to capture, create, and share knowledge / best practices  Data stewardship standards and processes that ensure consistency of data quality, storage, and exchange BioPharma and other industry players have demonstrated innovative, peer-leading Search, Access, and Architecture capabilities Capability Maturity Stages Basic Developing Functional Advanced World-class 1 2 3 4 5 Open Access Data Stewardship Data Structuring Key Data Assets Scientific Search Expertise Location ArchitectureSearch Access
  10. 10. 10 Basic Developing Functional Advanced World-class  Data access permissions that reflect a balance between security and accessibility  A culture of collaboration enables information access across divisions  Designated roles and responsibilities to champion data stewardship  Employees know what information to store and where to store it  Well defined best practices, search processes, and rules  Employees understand the search content and participate in helping steward data  Query experts help conducting complex searches  Intuitive tools and applications ensure all information is searchable  Well established processes for categorizing, structuring and storing information  Clearly defined data assets in key business areas  Well-defined links between key data assets to enable interoperability between different information types What does “good” Search look like for R&D? Addressing identified challenges will produce a future state with capable people, processes and technologies to enable fluid information exchange and better decision making 1 2 3 4 5 Current State Capability Maturity Stages Search Access Architecture Access Data Stewardship Data Structuring Key Data Assets Scientific Search Expertise Location ILLUSTRATIVE
  11. 11. 11 SIAS has developed a flexible, repeatable business engagement and problem solving approach Scope Pilot: Define scope of problem, including specific business impact and value proposition Define Requirements: Define use cases; prioritize and select use case(s) to test in Pilot Select / Model Use Case(s): Model information flow for selected use case(s), select pilot platform Execute Pilot(s): Build test environment; create / update processes / standards; test use case & determine if needs are met Build Business Case / Roadmap: Develop business case & roadmap for scale-up; validate with business users and sponsor Scale Solution: Expand coverage / capability to new information types, sources, users; measure adoption, performance, value realized Embed and Maintain: Assess long-term production viability; define long-term roadmap; take viable solutions to production scope / capability Monitor / Measure: Continue to track performance; re-visit unaddressed business issues Target and Engage Business Area: Build relationships in target areas; gauge IM needs Identify Pain Points: Document high level business processes, identify & map key information types & sources, characterize pain points Validate / Prioritize Issues: Define impact of pain points, detail / prioritize use cases aligned to business impacts, develop business case Solve (Pilot Solution) Execute Pilot(s) Define Requirements Scale and Embed Build Business Case / Roadmap Monitor / Measure Scope Pilot Model Use Case(s) Scale Solution Embed and Maintain Target & Engage Business Area Identify Pain Points Validate & Prioritize Issues Engage and Diagnose SIAS follows a consistent process for diagnosing and solving specific business area IM issues, then embedding and transitioning those solutions 1-6 months 6-18 months1-3 months
  12. 12. 12 Drive an integrated approach to improve Information Management & Search Targeted IM solutions: Deliver improvements in processes, technologies, and / or behaviors that improve data quality / availability Stewardship: A set of shared practices for creating, storing, sharing, and maintaining information that is conceived, sustained, and improved by business Information Stewards  Address complex, specific business needs with appropriate processes / capabilities  Deep coverage of information sources Search: Deploy a search capability to make information more accessible, explorable and useful for scientists  Addresses broad, high-level search use cases  Provide exploratory and analytic capabilities to drive value high ROI opportunities  Big Data framework that can deliver use cases beyond scalable search  Define, communicate, embed, and monitor good stewardship practices  Create a vital link between business, information, and technology
  13. 13. 13 Knowledge Assets Business Groups People Information Types Organization Units Sources Business Processes Decisions/ Gateways Activities Roles The R&D Information Landscape is increasingly complex
  14. 14. 14 sIFM is a method of documenting and modeling the flow of information through an enterprise (from data generation to knowledge creation) that allows both targeted analysis (e.g. information flow through a specific business process for a select organization), as well as holistic analysis (e.g. complex, cross- organizational information flows, processes, and knowledge transitions) across the information continuum. PPDM GHH MCC •Regulatory MRL MMD PharmSci Merck Traditional Business Analysis Multiple BA resources working to develop project/area-specific analysis artifacts using a variety of methods and representations (not connected; shared and stored in isolation) Multiple BA resources working to represent information flows in a common way, so that related information entities are connected, complex interactions can be visualized, understood and analyzed, and project/area-specific ‘views’ of the model can still be generated Semantic Information Flow Modeling custom Graphing Canvases Lead Optimization (LO) (fromIFDs) PCC-FIH (fromIFDs) Target ID/Validation (fromIFDs) Lead ID (fromIFDs) FIH-PH2B (fromIFDs) PH3-File (fromIFDs) File-Approval (fromIFDs) Approval - Launch (fromIFDs) Semantic Information Flow Modeling (sIFM)
  15. 15. 15 Results in disparate analysis artifacts (ppt, excel, word/text) with related information within them that aren’t linked
  16. 16. 16 Applying sIFM Ontologies / Taxonomies / Relationships Enhanced workflows, stewardship models Improved Integration, Search, Decision support Applying sIFM to represent and analyze complex information domains, and knowledge transitions, in order to successfully identify and implement technologies that enhance information/knowledge structure, interoperability, and search.
  17. 17. 17 Information Management Solution QUICK – Overview SIAS characterized several information management challenges which dictated the need for a knowledgebase of definitive pre-clinical compound data for Pharmacology / Drug Metabolism Dispersed Historical Data A lengthy, complicated process is required, on a regular basis, to retrieve information off hard-drives, shared drives, and outdated repositories Duplicative Data Capture / Processing The precedent of creating Excel copies of data for upload to Teamsites consumes resources and creates islands of potentially outdated data Access / Storage of Definitive Data Unable to effectively manage definitive data for compounds Challenges Incomplete Data Upload A large portion of the data generated is not uploaded into structured repositories Harmonizing Reporting Standards Inadequate governance over data upload protocols and non-standardized assay reporting formats limit data usability for cross-compound comparisons Solution QUantItative PharmaCology Knowledgebase (‘QUICK”)  Single, authoritative portal for access to definitive, integrated data sets of clinical and pre- clinical metabolism and in vivo pharmacology experimental results  Exposed data will be targeted, but not limited to, addressing hypothesis generating questions relating to predictive modeling such as human dose prediction, study avoidance, and BIC benchmarking of candidate selection, and translational PK/PD modeling  Data will be made available in a well-structured and searchable format allowing easy data representation and integration with existing and future data analysis and visualization tools Centralized & Structured Data Improved Retrieval & Access
  18. 18. 18 - 18 - Information Management Solution QUICK – Expected Value Improvement Opportunities Description Improve Data Collation / Reporting Efficiency for Definitive Pre-Clinical Data Reduce time to collate definitive datasets by ~95% Enhance Analytical Productivity and Opportunities 50-75% increase in efficiency of analysis (comparisons of results from prior assays) Enhance Collaboration Improved collaboration through stewardship and metadata management, increasing productivity by 50% for modeling and simulation; increased pharmacology / drug met. productivity Study Avoidance Potentially eliminate unnecessary studies due to faster access to more accurate definitive datasets, resulting in better study selection and confidence in progressing / killing compounds QUICK enables decisions to avoid costly studies through better design and decision making and greater productivity through better data quality, structure, and accessibility; improved data collation capability; and improved collaboration and cross-functional information sharing
  19. 19. 19 - 19 - Acknowledgements • SIAS • Informatics IT • MRL-IT • MRL • Deloitte

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